Slask Wroclaw vs ŁKS Łódź Statistics & Analysis

Slask Wroclaw

May 11, 2026 - 16:30

2 1.45

2 1.25

xG Accuracy: 70%

ŁKS Łódź

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Tracked markets vs full-time result

Each row compares the model’s highlighted side (or lean) to what happened at full time.

Market Prediction Result Outcome
Over / Under 2.5 Over 2.5 Over 2.5 (4 goals) ✔ Correct
Both Teams To Score BTTS Yes Yes ✔ Correct
1X2 Slask Wroclaw Draw ✖ Incorrect
Correct Score Insights 1-1 2-2 ✖ Incorrect

AI match briefing

AI Match Summary

Below is a compact, numbers-first snapshot aligned with the same engine as the cards above.

League: I Liga
Fixture: Slask Wroclaw vs ŁKS Łódź
Kickoff: 2026-05-11 16:30:00
1X2 (model): Home 41.8% · Draw 25.7% · Away 32.6%
xG (showing): Slask Wroclaw 1.45 — ŁKS Łódź 1.25 (total xG ≈ 2.7)
Primary / headline line (Betting Primary Pick when shown): BTTS Yes
Model: 81.1% · Implied: 59.7% · Probability edge: +21.4 pts · Est. EV: +28.1%
BTTS (model): Yes 81.1% · No 18.9%
Correct score (top bin): 1-1 (10.2%)

Totals and BTTS are evaluated against current market prices where available.

Early match state can move realised goals away from pre-kick projections.

Best Bet + Reason

Primary pick from the decision engine: BTTS Yes.

Model probability is compared to implied probability from odds to highlight a probability edge; EV uses the same model probability with the best decimal price tracked.

No pick is a guarantee; variance is especially large in scoreline markets.

FAQ

Is the most likely correct score a good bet?

Usually no as a standalone bet: the “most likely” scoreline is still a low absolute probability tail event (often single digits, sometimes low teens). Use it as context; keep any correct-score stake in the “fun / small” bucket.

What changes first if odds move?

Implied probabilities and EV move immediately with price; model probabilities in this snapshot do not update until the pipeline is re-run. Refresh after material line moves.

How should I read EV versus a probability gap?

Probability edge = model probability minus implied probability (reported here in percentage points). EV ≈ model probability × best tracked decimal odds − 1, shown as return per unit stake. They are related but not interchangeable labels.

Who has the edge in the match-winner market?

Use the 1X2 model percentages in the summary and the 1X2 market card: the side with the highest model % is the model lean, but check EV — a lean can still be -EV after prices.

Risk Factors

Price movement: implied probabilities and EV move with odds.
Sample / data gaps: low-information leagues widen forecast bands.
In-play state: goals and red cards are not modelled here.
Scoreline variance: the most likely scoreline is still usually a low absolute probability outcome (often well below 20%).

Methodology

Inputs: Same structured facts bundle as the public prediction page (xG / Poisson snapshot, market EV where available, decision engine v2).
Compliance: Educational framing only; not personalised advice.

Last Updated

May 17, 2026 (UTC)

How to use this

Focus on the Primary line when you want one actionable idea.
Do not parlay many thin-edge picks together; edges do not add reliably.
Treat longshots as optional, high-stake-sizing plays only.

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Back to Statistics

I Liga — Standings

#	TEAM	MP	W	D	L	PTS
1	Wisla Krakow	33	19	11	3	68
2	Slask Wroclaw	32	16	10	6	58
3	Wieczysta Kraków	33	16	8	9	56
4	Chrobry Głogów	33	16	6	11	54
5	ŁKS Łódź	32	14	9	9	51
6	Ruch Chorzów	32	13	11	8	50
7	Polonia Warszawa	33	14	8	11	50
8	Miedz Legnica	32	14	7	11	49
9	Polonia Bytom	32	13	8	11	47
10	Puszcza Niepołomice	32	11	13	8	46
11	Odra Opole	33	11	11	11	44
12	Pogoń Grod. Mazowiecki	32	11	10	11	43
13	Stal Rzeszów	33	12	6	15	42
14	Pogoń Siedlce	33	9	9	15	36
15	Stal Mielec	33	9	6	18	33
16	Górnik Łęczna	33	5	12	16	27
17	Znicz Pruszków	33	6	7	20	25
18	Tychy 71	32	5	6	21	21

#	TEAM	MP	GS	GC	+/-	PTS
1	Wisla Krakow	33	70	32	+38	68
2	Wieczysta Kraków	33	69	46	+23	56
3	Slask Wroclaw	32	66	46	+20	58
4	Polonia Bytom	32	54	43	+11	47
5	ŁKS Łódź	32	53	46	+7	51
6	Pogoń Grod. Mazowiecki	32	51	54	-3	43
7	Ruch Chorzów	32	50	42	+8	50
8	Polonia Warszawa	33	50	48	+2	50
9	Miedz Legnica	32	49	50	-1	49
10	Chrobry Głogów	33	47	35	+12	54
11	Stal Mielec	33	47	61	-14	33
12	Stal Rzeszów	33	46	57	-11	42
13	Puszcza Niepołomice	32	43	38	+5	46
14	Górnik Łęczna	33	38	59	-21	27
15	Znicz Pruszków	33	37	66	-29	25
16	Tychy 71	32	37	71	-34	21
17	Odra Opole	33	33	38	-5	44
18	Pogoń Siedlce	33	33	41	-8	36